# ----
# GBDT分析时间序列数据
# ----


# 模拟数据 --一个最小而成回归的模拟
N <- 1000
X1 <- runif(N)
X2 <- 2*runif(N)
X3 <- ordered(sample(letters[1:4],N,replace=TRUE),levels=letters[4:1])
X4 <- factor(sample(letters[1:6],N,replace=TRUE))
X5 <- factor(sample(letters[1:3],N,replace=TRUE))
X6 <- 3*runif(N) 
mu <- c(-1,0,1,2)[as.numeric(X3)]
SNR <- 10 # signal-to-noise ratio
Y <- X1**1.5 + 2 * (X2**.5) + mu
sigma <- sqrt(var(Y)/SNR)
Y <- Y + rnorm(N,0,sigma)
# introduce some missing values
X1[sample(1:N,size=500)] <- NA
X4[sample(1:N,size=300)] <- NA
data <- data.frame(Y=Y,X1=X1,X2=X2,X3=X3,X4=X4,X5=X5,X6=X6)

# ----
# 训练模型
# ----
library(gbm)
gbm1 <-
  gbm(Y~X1+X2+X3+X4+X5+X6,      # formula
      data=data,        # dataset
      var.monotone=c(0,0,0,0,0,0),    # -1: monotone decrease, +1: monotone increase,
      # 0: no monotone restrictions
      distribution="gaussian",     # see the help for other choices
      n.trees=1000,             # number of trees
      shrinkage=0.05,            # shrinkage or learning rate, 0.001 to 0.1 usually work
      interaction.depth=3,         # 1: additive model, 2: two-way interactions, etc.
      bag.fraction = 0.5,         # subsampling fraction, 0.5 is probably best
      train.fraction = 0.5,       # fraction of data for training, first train.fraction*N used for training
      n.minobsinnode = 10,         # minimum total weight needed in each node
      cv.folds = 3,             # do 3-fold cross-validation
      keep.data=TRUE,           # keep a copy of the dataset with the object
      verbose=FALSE,            # don't print out progress
      n.cores=1)               # use only a single core (detecting #cores is error-prone, so avoided here)

# 通过k折检验模型的好坏
best.iter <- gbm.perf(gbm1,method="cv")
print(best.iter)

# summary模型
summary(gbm1,n.trees=best.iter) # based on the estimated best number of trees

